Introduction
Population aging is the most important demographic challenge and one of the main elements of social transformations in the first half of the 21st century, and its influence is expected to increase in the future [1]. The rate of increase in population aging in Iran is more than the global level. This rate, which has been at the same level as the countries in West Asia and North Africa, has surpassed the level of countries in the region since the 2010s and will surpass the global level by the 2040s [3, 4]. Considering the importance of the changes that population aging will create in Iranian society, this study aims to investigate the challenges and drivers of population aging in the next three decades in Iran.

Methods
The is a futures study using the scenario analysis method. To understand the possible challenges of the population aging, data and megatrends were collected from different sources using a scoping review method. Then the obtained information was obtained using the opinion of experts. A checklist of selected challenges was completed by 22 experts who were asked to gave 1 to 10 points for each challenge in the future (next 30 years) in terms of importance and uncertainty. Then, the obtained information was analyzed based on experts’ opinions, structural analysis, cross-impact balance analysis, and scenario development.
For the structural analysis of the matrix of direct influence, the Mic-Mac method was used in Mic-Mac software. This matrix, which is an n*n matrix, is called the direct impact matrix, where n shows the number of variables influencing the future. In the final stage of the research, to examine the scenarios, the total number of possible scenarios was equal to the multiplication of the number of alternatives of each key uncertainty. As the number of key uncertainties or alternatives for each variable increases, the number of scenarios increases exponentially. These number of possible scenarios do not have the same value; therefore, the scoring of the scenarios was done by using the Scenario Wizard software. Based on the cross-impact balance methodology [24، 25], to select the appropriate scenario, the inconsistency score as well as the total effects related to the scenarios were calculated and extracted. The data were analyzed using Mic-Mac and Scenario Wizard applications. Finally, the scenarios were developed by the panel of experts and policy proposals were presented to achieve the desired scenario.

Results
In the first step, based on environmental monitoring, reviewing the policies related to population aging in selected countries and policy documents, secondary data analysis of censuses and national surveys data, the challenges of population aging were identified. After the initial screening, the number of challenges was reduced to 114, which were classified in eight areas. Most of the challenges were related to economic, social and health areas. After collecting the experts’ opinions, finally, 13 items were selected as key variables in shaping the future of Iran’s population aging which were: universal insurance coverage and pension, economic participation and labor market, management and governance in ageing, social participation, distribution of welfare, geriatric workforce, health and medical costs, coverage and quality of health services, fertility and household size, intergenerational relationships, pension funds, quality of life, and lifestyle. Their direct impact matrix was entered into the Mic-Mac software after completion by the experts. 
The variables of management and governance in ageing, pension funds, health costs, distribution of welfare, fertility and household size, and insurance coverage were identified as influencing factors. The variable of economic participation was the risk factor. The variables of lifestyle, quality of life, intergenerational relationships, and social participation were the outputs of the system, which are influenced by influencing factors. 
According to the outputs of Mic-Mac software and using the DEMATEL method, the experts chose the most effective variables to enter the Scenario Wizard software as following: 1- management and governance in ageing, 2- pension funds, 3- economic participation and labor market, 4- distribution of welfare, 5- fertility and household size, 6- health costs, 7- insurance coverage and pension. These were considered as the main determinants or drivers. It should be noted that, to avoid complicating the analysis, the two factors of health costs and insurance coverage & pension were combined with the factor of the distribution of welfare. The possible scenarios for the future of population aging in 2050 were “Risky ageing”, “ Twilight ageing”, “Dawn of ageing”, and “Prosperous ageing”.

Conclusion
According to the findings of this study, “risky ageing” and “prosperous ageing” are pessimistic and optimistic scenarios of the future of population aging in Iran, and two scenarios of Twilight ageing”, “Dawn of ageing” are intermediate scenarios, the first one towards risky ageing and the second one towards prosperous ageing. Prosperous ageing is a favorable scenario for the future of Iran’s population aging against the unfavorable scenario of risky ageing. The population of Iran will reach old age by the 2040s which will lead to challenges in various economic, social, demographic and health aspects. To achieve prosperous ageing in Iran, we need a smart governance with a forward-looking view in the field of aging, equal welfare distribution, win-win pension funds, active economic and social participation of older adults, and fertility rate above the replacement level. A comprehensive policy approach to all aspects of prosperous aging is a necessity for the future of population aging in Iran.

Ethical Considerations

Compliance with ethical guidelines
This study was approved by the ethics committee of the University of Social Welfare And Rehabilitations Sciences (Code: IR.USWR.RE.1401.178).

Funding
This study was funded by Rahman Institute in Tehran, Iran.

Authors' contributions
Conceptualization and methodology: Nasibeh Zanjari  and Seyedeh Zahra Kalantari Banadaki; Data analysis: Nasibeh Zanjari, Seyedeh Zahra Kalantari Banadaki and Rasoul Sadeghi; Supervision: Nasibeh Zanjari; Literature review, resources, draft preparation, and review: All authors.

Conflicts of interest
The authors declared no conflict of interest.

Acknowledgments
The authors would like to thank the Rahman Institute for their financial support.



 

1. Rowland DT. Population aging: The transformation of societies. Berlin: Springer Science & Business Media; 2012. [DOI:10.1007/978-94-007-4050-1]
2. Coulmas F. Population decline and ageing in Japan-the social consequences. London: Routledge; 2007. [DOI:10.4324/9780203962022]
3. UNFPA. Perspectives on population ageing in the Asia-pacific region. Bangkok: UNFPA Asia and the Pacific Regional Office; 2017. [Link]
4. McNicoll G. World Population Ageing 1950-2050. Population and Development Review. 2002; 28(4):814-6. [Link]
5. Department of Economic and Social Affairs, John Richard Wilmoth, United Nations Department of Economic and Social Affairs, Daniela Bas, Santanu Mukherjee, Naveed Hanif. World social report 2023: Leaving no one behind in an ageing world. New York: UN; 2023. [Link]
6. Sadeghi R. [Age structure transitions and emerging demographic window in Iran: Economic outcomes and policy implications (Persian)]. Women’s Strategic Studies. 2012; 14(55):95-150. [Link]
7. Clark RL, Ogawa N, Mason A. Population aging, intergenerational transfers and the macroeconomy. Cheltenham: Edward Elgar Publishing; 2007. [Link]
8. Gavigan JP, Scapolo F, Keenan MMI, Farhi FLD, Capriati M. FOREN: Guide-foresight for regional development network: A practical guide to regional foresight. Brussels: European Commission; 2001. [Link]
9. Mollaei MM, Talebian H. [Forsight of Iran challenges by Structural analysis method (Persian)]. Majles and Rahbord Journal. 1395; 23(86):32-5. [Link]
10. Miles I, Keenan M. Practical guide to regional foresight in the United Kingdom. Brussels: European Commission; 2002. [Link]
11. Martin R Ben. Technology foresight: Capturing the benefits from science-related technologies. Research Evaluation. 1996; 6(2):158-68. [DOI:10.1093/rev/6.2.158]
12. Keeler LW, Bernstein MJ. The future of aging in smart environments: Four scenarios of the United States in 2050. Futures. 2021; 133:102830. [DOI:10.1016/j.futures.2021.102830]
13. Shergold I, Lyons G, Hubers C. Future mobility in an ageing society-Where are we heading? Journal of Transport & Health. 2015; 2(1):86-94. [DOI:10.1016/j.jth.2014.10.005]
14. Inayatullah S. Ageing: alternative futures and policy choices. Foresight. 2003; 5(6):8-17. [DOI:10.1108/14636680310698793]
15. Rezvani Khaledi F, Pedram A. [Alternative futures of population ageing in Iran with causal layered analysis (Persian)]. Interdisciplinary Studies on Strategic Knowledge. 2020; 3(12):373-404. [Link]
16. Adib Roshan F, Peymanizad H, Talebpour M, pourezzat AA. [Improving the image of future for the elderly of 2050, scenario based (Persian)]. Journal of Iran Futures Studies. 2020; 4(2):229-61. [DOI:10.30479/jfs.2020.11564.1115]
17. FatehRad M, Jalilvand R, Mowlaei M, Samiie S, Nasrolahi Vasati L. [Methodological coordinates of transdisciplinary futures studies as an integrated meta-paradigm (Persian)]. Interdisciplinary Studies in the Humanities. 2014; 6(1):35-161. [DOI:10.7508/isih.2014.21.007]
18. Haghdoost A, Alikhani A, Hosseini Golkar M, Dehnavieh R, Seifi S. [Scenarios of COVID-19 epidemic effects on socioeconomic and health status of Iran (Persian)]. Iranian Journal of Epidemiology. 2022; 18(2):104-15. [Link]
19. Glenn JC, Gordon TJ. Futures research methodology-version 3-0. Washington: Millennium Project; 2009. [Link]
20. Schwartz P. Art of the long view: Planning for the future in an uncertain world. Hoboken: Wiley; 1998. [Link]
21. Arksey H,  O'Malley L. Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology. 2005; 8(1):19–32. [Link]
22. Miles I, Keenan M, Koi-Ova J. Handbook of knowledge society. Loughlinstown: Eurofound; 2003. [Link]
23. Fontela E, Gabus A. World problems an invitation to further thought within the framework of DEMATEL. Geneva: Battelle Geneva Research Centre; 1972. [Link]
24. Weimer-Jehle W. Cross-impact balances: A system-theoretical approach to cross-impact analysis. Technological Forecasting and Social Change. 2006; 73(4):334-61. [DOI:10.1016/j.techfore.2005.06.005]
25. Weimer-Jehle WJapa. Properties of cross-impact balance analysis. 2009 [Unpublished]. [DOI:10.48550/arXiv.0912.5352]
26. Zanjari N, Sadeghi R. [Measuring of older adults’ well-being in provinces of iran using agewatch index (Persian)]. Salmand. 2022; 16 (4):498-517. [DOI:10.32598/sija.2022.16.4.2814.1]
27. Karimian Sichani Z, Cheraghali MH, Dehghani A. Impact analysis of the early retirement in Iranian social security pension fund using system dynamics on the horizon 2051. Journal of Iran Futures Studies. 2022; 7(1):213-40. [DOI:10.30479/JFS.2022.16188.1340]
28. Abbasi-Shavazi MJ, Morgan SP, Hossein-Chavoshi M, McDonald P. Family change and continuity in iran: Birth control use before first pregnancy. Journal of Marriage and the Family. 2009 71(5):130924. [DOI:10.1007/978-90-481-3198-3] [PMID] [PMCID]
29. Victor C. The social context of ageing: A textbook of gerontology. London: Routledge; 2004. [DOI:10.4324/9780203338599]
30. Gholami A. [Explaining the status of pension funds (Persian)]. Economic Security. 2022; 10(1-2):99-110. [Link]
31. Najjarpour H, Nili Ahmadabadi M. [Prioritize identified factors and resources of the social security organization to establish of multi-pillar social security system with combined approach of DANP and scenario writing (Persian)]. Tarbiat Modares University. 2021; 11(3):151-72. [Link]
32. Eini Zeinab H. [Esimating total fertility rate and its components in Iran; using multivariate demographic method (Persian)]. Iranian Population Studies. 2013; 1(2):9-42. [Link]
33. Kalache A, Gatti A. Active ageing: A policy framework. Advances in Gerontology. 2003; 11:7-18. [PMID]